A feedback reduced random beamforming algorithm based on opportunistic eigenbeamforming system

Opportunistic beamforming(OBF) is a powerful technique that exploits multiuser diversity to increase the system throughput on the downlink correlated wireless channels. Opportunistic eigenbeamforming(OEB) can achieve larger multiuser diversity gain than OBF under different degrees of channel correlation but it need additional feedback. This paper proposes a feedback reduced random beamforming algorithm based on OEB. In the proposed method, each user computes the correlation factor of the beamforming vectors generated by OBF and OEB. Those users whose correlation factor is greater than the predefined threshold feed back their SNRs, others don´t need to feed back SNRs, thus the base station choose the best user to transmit data. Simulation results show that the performance of the proposed algorithm is asymptotic to that of OEB while the overall feedback is significantly reduced.